Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery最新文献_第2页

Using histograms to detect and track objects in color video 利用直方图检测和跟踪彩色视频中的对象

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991219

Michael Mason, Zoran Duric

引用次数: 127

High storage capacity architecture for pattern recognition using an array of Hopfield neural networks 使用Hopfield神经网络阵列的模式识别的高存储容量架构

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991221

Ming-Jung Seow, V. Asari

{"title":"High storage capacity architecture for pattern recognition using an array of Hopfield neural networks","authors":"Ming-Jung Seow, V. Asari","doi":"10.1109/AIPR.2001.991221","DOIUrl":"https://doi.org/10.1109/AIPR.2001.991221","url":null,"abstract":"A new approach for the recognition of images using a two dimensional array of Hopfield neural networks is presented in this paper. In the proposed method, the N/spl times/N image is divided into sub-blocks of size M/spl times/M. Two-dimensional Hopfield neural networks of size M/spl times/M are used to learn and recognize the sub-images. All the N/sup 2//M/sup 2/ Hopfield modules are functioning independently and are capable of recognizing the corrupted image successfully when they work together. It is shown mathematically that the network system converges in all circumstances. The performance of the proposed technique is evaluated by applying it into various binary and gray scale images. The gray scale images are treated in a three-dimensional perspective by considering an 8-bit gray scale image as 8 independent binary images. Eight layers of binary networks are used for the recognition purpose. A Fuzzy-ART based neural network is used for the classification and labeling of the outputs in the Hopfield network. By employing the new approach, it can be seen that the storage capacity of the entire pattern recognition system would be increased to 2/sup n/ where n=N/sup 2//M/sup 2/. Experiments conducted on different images of various sizes have shown that the proposed network structure can learn and recognize images even with 30% noise. In addition, the number of iterations required for the convergence of the network is significantly reduced and the number of synaptic weights required for the entire architecture is reduced from N/sup 4/ to N/sup 2/M/sup 2/. The proposed network structure is suitable for building dedicated hardware to enable the pattern recognition in real-time due to the requirement of less number of registers to store synaptic weights and reduced number of interconnections between neurons.","PeriodicalId":277181,"journal":{"name":"Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115391259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A conversational paradigm for multimodal human interaction 多模式人类互动的对话范例

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991207

Francis K. H. Quek

引用次数: 3

Suitability of synthetic imagery for ATR evaluation 综合图像对ATR评价的适用性

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991203

D. Meredith, C. Walters, C. Hoover

引用次数: 0

Mixture of principal axes registration for change analysis in computer-aided diagnosis 计算机辅助诊断中变化分析的混合主轴配准

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991199

R. Srikanchana, Kun Huang, J. Xuan, M. Freedman, Y. Wang

{"title":"Mixture of principal axes registration for change analysis in computer-aided diagnosis","authors":"R. Srikanchana, Kun Huang, J. Xuan, M. Freedman, Y. Wang","doi":"10.1109/AIPR.2001.991199","DOIUrl":"https://doi.org/10.1109/AIPR.2001.991199","url":null,"abstract":"Non-rigid image registration is a prerequisite for many medical image analysis applications, such as image fusion of multi-modality images and quantitative change analysis of a temporal sequence in computer-aided diagnosis. By establishing the point correspondence of the extracted feature points, it is possible to recover the deformation using nonlinear interpolation methods such as the thin-plate-spline approach. However, it is a difficulty task to establish an exact point correspondence due to the high complexity of the nonlinear deformation existing in medical images. In this paper, a mixture of principal axes registration (mPAR) method is proposed to resolve the correspondence problem through a neural computational approach. The novel feature of mPAR is to align two point sets without needing to establish an explicit point correspondence. Instead, it aligns the two point sets by minimizing the relative entropy between their probability distributions, resulting in a maximum likelihood estimate of the transformation matrix. The registration process consists of: (1) a finite mixture scheme to establish an improved point correspondence and (2) a multilayer perceptron (MLP) neural network to recover the nonlinear deformation. The neural computation for registration used a committee machine to obtain a mixture of piecewise rigid registrations, which gives a reliable point correspondence using multiple extracted objects in a finite mixture scheme. Then the MLP was used to determine the coefficients of a polynomial transform using extracted cross-points of elongated structures as control points. We have applied our mPAR method to a temporal sequence of mammograms from a single patient. The experimental results show that mPAR not only improves the accuracy of the point correspondence but also results in a desirable error-resilience property for control point selection errors.","PeriodicalId":277181,"journal":{"name":"Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125055581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A qualitative image reconstruction from an axial image sequence 从轴向图像序列的定性图像重建

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991222

Philippe Guermeur, E. Pissaloux

引用次数: 5

A basic hand gesture control system for PC applications 一个基本的手势控制系统的PC应用程序

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991206

C. Cohen, G. Beach, G. Foulk

引用次数: 24

A multiple perspective spectral approach to object detection 目标检测的多视角光谱方法

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991212

R. Bonneau

引用次数: 0

PUPILS-enabling a dialogue between the machine and the brain 瞳孔——让机器和大脑对话

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991208

R. Heishman, Zoran Duric, H. Wechsler

{"title":"PUPILS-enabling a dialogue between the machine and the brain","authors":"R. Heishman, Zoran Duric, H. Wechsler","doi":"10.1109/AIPR.2001.991208","DOIUrl":"https://doi.org/10.1109/AIPR.2001.991208","url":null,"abstract":"The human eye has been called the window to the soul. One component of the eye, the pupil, is considered by some psychologists to be the single involuntary indicator of cognitive activity in the human brain. Much research is currently directed toward eye tracking for the purpose of determining the focus of a subject's attention. This information is then used in various HCI applications (e.g., to operate a system GUI visually). Our efforts in this area are quite unique in that the goal is to use the pupil response as a measure of attentiveness to a particular visual task. We first discuss the role of the pupil in this capacity and the feasibility of a system designed to monitor and interpret pupil response relative to specific visual stimuli. We then demonstrate our approach in implementing a machine vision system (PUPILS-PUPil InterLocution System) that endeavors to gauge a subject's degree of attentiveness relative to a specific visual task. Finally, we present the results of initial experiments using PUPILS, discuss the contributions and shortcomings of this initial prototype and expound our plan for continued research in this area.","PeriodicalId":277181,"journal":{"name":"Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127177901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Directional edge registration for temporal chest image subtraction 方向边缘配准用于时间胸部图像减法

Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery Pub Date : 2001-10-10 DOI: 10.1109/AIPR.2001.991200

Hui Zhao, S. Lo, M. Freedman

{"title":"Directional edge registration for temporal chest image subtraction","authors":"Hui Zhao, S. Lo, M. Freedman","doi":"10.1109/AIPR.2001.991200","DOIUrl":"https://doi.org/10.1109/AIPR.2001.991200","url":null,"abstract":"We used a directional filtering technique to accurately register the ribs on temporal chest radiographs. Rib registration was the primary technical objective. In order to accurately extract the ribs, we developed a directional edge function that used rotating kernels through a dynamic search technique to extract rib edges of varying contrast. A directionally-weighted operator with a phase-contrast feature is embedded in the kernel design, which allows us to separate the upper and lower bounds of structures in the vertical direction. Broken rib edges were reconnected using a reasoning algorithm by analyzing the rib position and curve. The lung and heart boundaries were extracted using an inverted umbrella filter. Control points were designated at turning points and distributed on smooth edge segments on the temporal images. A thin-plate spline technique took the control points and performed the final registration prior to the temporal image subtraction. We found that normal chest structures (i.e. ribs, heart and normal lung structures) were greatly reduced in the subtraction images. In addition, the contrast of cancer spots was significantly increased.","PeriodicalId":277181,"journal":{"name":"Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115216594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3